At a Glance
- Tasks: Lead a new ML Engineering team and build scalable ML infrastructure.
- Company: SPG Resourcing, a forward-thinking company in the tech space.
- Benefits: Competitive salary, flexible hybrid work, and comprehensive benefits package.
- Other info: Exciting opportunity for career growth in a collaborative environment.
- Why this job: Join a dynamic team and shape the future of machine learning.
- Qualifications: 5+ years in machine learning with production deployment experience.
The predicted salary is between 70000 - 90000 £ per year.
SPG Resourcing is seeking a Machine Learning Engineering Manager to lead a new ML Engineering team in York or London. This role involves building scalable ML infrastructure and managing the end-to-end MLOps lifecycle.
Ideal candidates will have over 5 years of experience in machine learning with a strong background in production deployments, especially on GCP Vertex AI. The position is hybrid, offering flexibility, alongside a competitive salary and benefits package.
ML Engineering Manager — Lead Production ML (Hybrid) employer: SPG Resourcing
SPG Resourcing is an excellent employer, offering a dynamic work culture that fosters innovation and collaboration in the rapidly evolving field of machine learning. With a focus on employee growth, we provide ample opportunities for professional development and a competitive benefits package, all within the vibrant cities of York or London, making it an ideal location for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineering Manager — Lead Production ML (Hybrid)
✨Tip Number 1
Network like a pro! Reach out to folks in the ML community, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving GCP Vertex AI. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and MLOps best practices. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace ML Engineering Manager — Lead Production ML (Hybrid)
Some tips for your application 🫡
Show Off Your Experience:When you're writing your application, make sure to highlight your experience in machine learning and production deployments. We want to see how your background aligns with the role, especially if you've worked with GCP Vertex AI.
Tailor Your Application:Don’t just send a generic application! Take the time to tailor your CV and cover letter to reflect the specific skills and experiences mentioned in the job description. This shows us that you’re genuinely interested in the position.
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that are easy to read. Use bullet points where necessary to break down your achievements and skills.
Apply Through Our Website:Make sure to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensures it reaches the right people!
How to prepare for a job interview at SPG Resourcing
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially around production deployments and MLOps. Be ready to discuss your experience with GCP Vertex AI in detail, as this will likely come up during the interview.
✨Showcase Your Leadership Skills
As a Machine Learning Engineering Manager, you'll need to demonstrate your ability to lead a team. Prepare examples of how you've successfully managed projects or teams in the past, focusing on your leadership style and how you motivate others.
✨Understand the Company’s Vision
Research SPG Resourcing and their goals in the ML space. Knowing their projects and how your role fits into their vision will show that you're genuinely interested and can contribute to their success.
✨Prepare Questions
Have a few thoughtful questions ready to ask at the end of the interview. This could be about the team dynamics, the challenges they face in building scalable ML infrastructure, or how they measure success in this role. It shows you're engaged and thinking critically about the position.
